Confluent Developer ft. Tim Berglund, Adi Polak & Viktor Gamov

Is it possible to manage and test data like code? lakeFS is an open-source data version control tool that transforms object storage into Git-like repositories, offering teams a way to use the same workflows for code and data. In this episode, Kris sits down with guest Adi Polak, VP of DevX at Treeverse, to discuss how lakeFS can be used to facilitate better management and testing of data.At its core, lakeFS provides teams with better data management. A theoretical data engineer on a large team runs a script to delete some data, but a bug in the script accidentally deletes a lot more data than intended. Application engineers can checkout the main branch, effectively erasing their mistakes, but without a tool like lakeFS, this data engineer would be in a lot of trouble.Polak is quick to explain that lakeFS isn’t built on Git. The source code behind an application is usually a few dozen mega bytes, while lakeFS is designed to handle petabytes of data; however, it does use Git-like semantics to create and access versions so adoption is quick and simple.Another big challenge that lakeFS helps teams tackle is reproducibility. Troubleshooting when and where a corruption in the data first appeared can be a tricky task for a data engineer, when data is constantly updating. With lakeFS, engineers can refer to snapshots to see where the product was corrupted, and rollback to that exact state.lakeFS also assists teams with reprocessing of historical data. With lakeFS data can be reprocessed on an isolated branch, before merging, to ensure the reprocessed data is exposed atomically. It also makes it easier to access the different versions of reprocessed data using any tag or a historical commit ID.Tune in to hear more about the benefits of lakeFS.EPISODE LINKSAdi Polak's TwitterlakeFS Git-for-data GitHub repo What is a Merkle Tree?If Streaming Is the Answer, Why Are We Still Doing Batch?Current 2022 sessions and slidesSign up for updates on Current 2023Watch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

How does leader election work in Apache Kafka®? For the past 2 ½ years, Adithya Chandra, Staff Software Engineer at Confluent, has been working on Kafka scalability and performance, specifically partition leader election. In this episode, he gives Kris Jenkins a deep dive into the power of leader election in Kafka replication, why we need it, how it works, what can go wrong, and how it's being improved.Adithya explains that you can configure a certain number of replicas to be distributed across Kafka brokers and then set one of them as the elected leader - the others become followers. This leader-based model proves efficient because clients only have to write to the leader, who handles the replication process internally.But what happens when a broker goes offline, when a replica reassignment occurs, or when a broker shuts down? Adithya explains that when these triggers occur, one of the followers becomes the elected leader, and all the other replicas take their cue from the new leader. This failover reassignment ensures that messages are replicated effectively and efficiently with multiple copies across different brokers.Adithya explains how you can select a broker as the preferred election leader. The preferred leader then becomes the new leader in failure events. This reduces latency and ensures messages consistently write to the same broker for easier tracking and debugging.Leader failover cannot cover all failures, Adithya says. If a broker can’t be reached externally but can talk to other brokers in the cluster, leader failover won’t be triggered. If a broker experiences transient disk or network issues, the leader election process might fail, and the broker will not be elected as a leader. In both cases, manual intervention is required.Leadership priority is an important feature of Confluent Cloud that allows you to prioritize certain brokers over others and specify which broker is most likely to become the leader in case of a failover. This way, we can prioritize certain brokers to ensure that the most reliable broker handles more important and sensitive replication tasks. Additionally, this feature ensures that replication remains consistent and available even in an unexpected failure event.Improvements to this component of Kafka will enable it to be applied to a wide variety of scenarios. On-call engineers can use it to mitigate single-broker performance issues while debugging. Network and storage health solutions can use it to prioritize brokers. Adithya explains that preferred leader election and leadership failover ensure data is available and consistent during failure scenarios so that Kafka replication can run smoothly and efficiently.EPISODE LINKSData Plane: Replication ProtocolOptimizing Cloud-Native Apache Kafka Performance ft. Alok Nikhil and Adithya ChandraSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

Are bad customer experiences really just data integration problems? Can real-time data streaming and machine learning be democratized in order to deliver a better customer experience? Airy, an open-source data-streaming platform, uses Apache Kafka® to help business teams deliver better results to their customers. In this episode, Airy CEO and co-founder Steffen Hoellinger explains how his company is expanding the reach of stream-processing tools and ideas beyond the world of programmers.Airy originally built Conversational AI (chatbot) software and other customer support products for companies to engage with their customers in conversational interfaces. Asynchronous messaging created a large amount of traffic, so the company adopted Kafka to ingest and process all messages & events in real time.In 2020, the co-founders decided to open source the technology, positioning Airy as an open source app framework for conversational teams at large enterprises to ingest and process conversational and customer data in real time. The decision was rooted in their belief that all bad customer experiences are really data integration problems, especially at large enterprises where data often is siloed and not accessible to machine learning models and human agents in real time.(Who hasn’t had the experience of entering customer data into an automated system, only to have the same data requested eventually by a human agent?)Airy is making data streaming universally accessible by supplying its clients with real-time data and offering integrations with standard business software. For engineering teams, Airy can reduce development time and increase the robustness of solutions they build.Data is now the cornerstone of most successful businesses, and real-time use cases are becoming more and more important. Open-source app frameworks like Airy are poised to drive massive adoption of event streaming over the years to come, across companies of all sizes, and maybe, eventually, down to consumers.EPISODE LINKSLearn how to deploy Airy Open Source - or sign up for an Airy Cloud test instanceGoogle Case Study about Airy & TEDi, a 2,000 store retailerBecome an Expert in Conversational EngineeringSupercharging conversational AI with human agent feedback loopsIntegrating all Communication and Customer Data with Airy and ConfluentHow to Build and Deploy Scalable Machine Learning in Production with Apache KafkaSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

The past year saw new trends emerge in the world of data streaming technologies, as well as some unexpected and novel use cases for Apache Kafka®. New reflections on the future of stream processing and when companies should adopt microservice architecture inspired several talks at this year’s industry conferences. In this episode, Kris is joined by his colleagues Danica Fine, Senior Developer Advocate, and Robin Moffatt, Principal Developer Advocate, for an end-of-year roundtable on this year’s developments and what they want to see in the year to come.Robin and Danica kick things off with a discussion of the year’s memorable conferences. Talk submissions for Kafka Summit London and Current 2022 featuring topics were noticeably more varied than previous years, with fewer talks focused on the basics of Kafka implementation. Many abstracts featured interesting and unusual use cases, in addition to detailed explanations on what went wrong and how others could avoid the same issues.The conferences also made clear that a lot of companies are adopting or considering stream-processing solutions. Are we close to a future where streaming is a part of everything we do? Is there anything helping streaming become more mainstream? Will stream processing replace batch?On the other hand, a lot of in-demand talks focused on the importance of understanding the best practices supporting data mesh and understanding the nuances of the system and configurations. Danica identifies this as her big hope for next year: No more Kafka developers pursuing quick fixes. “No more band aid fixes. I want as many people as possible to understand the nuances of the levers that they're pulling for Kafka, whatever project they're building.”Kris and Robin agree that what will make them happy in 2023 is seeing broader, more diverse client libraries for Kafka. “Getting away from this idea that Kafka is largely a Java shop, which is nonsense, but there is that perception.”Streaming Audio returns in January 2023.EPISODE LINKSPut Your Data To Work: Top 5 Data Technology Trends for 2023Write What You Know: Turning Your Apache Kafka Knowledge into a Technical TalkCommon Apache Kafka Mistakes to AvoidPractical Data Pipeline: Build a Plant Monitoring System with ksqlDBIf Streaming Is the Answer, Why Are We Still Doing Batch?View sessions and slides from Current 2022Watch the video version of this podcastKris Jenkins’ TwitterSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

Entomophiliac, Anna McDonald (Principal Customer Success Technical Architect, Confluent) has seen her fair share of Apache Kafka® bugs. For her annual holiday roundup of the most noteworthy Kafka bugs, Anna tells Kris Jenkins about some of the scariest, most surprising, and most enlightening corner cases that make you ask, “Ah, so that’s how it really works?”She shares a lot of interesting details about how batching works, the replication protocol, how Kafka’s networking stack dances with Linux’s one, and which is the most important Scala class to read, if you’re only going to read one.In particular, Anna gives Kris details about a bug that he’s been thinking about lately – sticky partitioner (KAFKA-10888). When a Kafka producer sends several records to the same partition at around the same time, the partition can get overloaded. As a result, if too many records get processed at once, they can get stuck causing an unbalanced workload. Anna goes on to explain that the fix required keeping track of the number of offsets/messages written to each partition, and then batching to force more balanced distributions.She found another bug that occurs when Kafka server triggers TCP Congestion Control in some conditions (KAFKA-9648). Anna explains that when Kafka server restarts and then executes the preferred replica leader, lots of replica leaders trigger cluster metadata updates. Then, all clients establish a server connection at the same time that lots TCP requests are waiting in the TCP sync queue.The third bug she talks about (KAFKA-9211), may cause TCP delays after upgrading…. Oh, that’s a nasty one. She goes on to tell Kris about a rare bug (KAFKA-12686) in Partition.scala where there’s a race condition between the handling of an AlterIsrResponse and a LeaderAndIsrRequest. This rare scenario involves the delay of AlterIsrResponse when lots of ISR and leadership changes occur due to broker restarts.Bugs five (KAFKA-12964) and six (KAFKA-14334) are no better, but you’ll have to plug in your headphones and listen in to explore the ghoulish adventures of Anna McDonald as she gives a nightmarish peek into her world of JIRA bugs. It’s just what you might need this holiday season!EPISODE LINKSKAFKA-10888: Sticky partition leads to uneven product msg, resulting in abnormal delays in some partitionsKAFKA-9648: Add configuration to adjust listen backlog size for AcceptorKAFKA-9211: Kafka upgrade 2.3.0 may cause tcp delay ack(Congestion Control)KAFKA-12686: Race condition in AlterIsr response handlingKAFKA-12964: Corrupt segment recovery can delete new producer state snapshotsKAFKA-14334: DelayedFetch purgatory not completed when appending as followerSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

Could you explain Apache Kafka® in ways that a small child could understand? When Mitch Seymour, author of Mastering Kafka Streams and ksqlDB, wanted a way to communicate the basics of Kafka and event-based stream processing, he decided to author a children’s book on the subject, but it turned into something with a far broader appeal.Mitch conceived the idea while writing a traditional manuscript for engineers and technicians interested in building stream processing applications. He wished he could explain what he was writing about to his 2-year-old daughter, and contemplated the best way to introduce the concepts in a way anyone could grasp.Four months later, he had completed the illustration book: Gently Down the Stream: A Gentle Introduction to Apache Kafka. It tells the story of a family of forest-dwelling Otters, who discover that they can use a giant river to communicate with each other. When more Otter families move into the forest, they must learn to adapt their system to handle the increase in activity.This accessible metaphor for how streaming applications work is accompanied by Mitch’s warm, painterly illustrations.For his second book, Seymour collaborated with the researcher and software developer Martin Kleppmann, author of Designing Data-Intensive Applications. Kleppmann admired the illustration book and proposed that the next book tackle a gentle introduction to cryptography. Specifically, it would introduce the concepts behind symmetric-key encryption, key exchange protocols, and the Diffie-Hellman algorithm, a method for exchanging secret information over a public channel.Secret Colors tells the story of a pair of Bunnies preparing to attend a school dance, who eagerly exchange notes on potential dates. They realize they need a way of keeping their messages secret, so they develop a technique that allows them to communicate without any chance of other Bunnies intercepting their messages.Mitch’s latest illustration book is—A Walk to the Cloud: A Gentle Introduction to Fully Managed Environments.  In the episode, Seymour discusses his process of creating the books from concept to completion, the decision to create his own publishing company to distribute these books, and whether a fourth book is on the way. He also discusses the experience of illustrating the books side by side with his wife, shares his insights on how editing is similar to coding, and explains why a concise set of commands is equally desirable in SQL queries and children’s literature.EPISODE LINKSMinimizing Software Speciation with ksqlDB and Kafka StreamsGently Down the Stream: A Gentle Introduction to Apache KafkaSecret ColorsA Walk to the Cloud: A Gentle Introduction to Fully Managed EnvironmenSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

What are the key factors to consider when developing event-driven architecture? When properly designed, events can connect existing systems with a common language and allow data exchange in near real time. They also help reduce complexity by providing a single source of truth that eliminates the need to synchronize data between different services or applications. They enable dynamic behavior, allowing each service or application to respond quickly to changes in its environment. Using events, developers can create systems that are more reliable, responsive, and easier to maintain.In this podcast, Adam Bellemare, staff technologist at Confluent, discusses the four dimensions of events and designing event streams along with best practices, and an overview of a new course he just authored. This course, called Introduction to Designing Events and Event Streams, walks you through the process of properly designing events and event streams in any event-driven architecture.Adam explains that the goal of the course is to provide you with a foundation for designing events and event streams. Along with hands-on exercises and best practices, the course explores the four dimensions of events and event stream design and applies them to real-world problems. Most importantly, he talks to Kris about the key factors to consider when deciding what events to write, what events to publish, and how to structure and design them to trigger actions like broadcasting messages to other services or storing results in a database.How you design and implement events and event streams significantly affect not only what you can do today, but how you scale in the future. Head over to Introduction to Designing Events and Event Streams to learn everything you need to know about building an event-driven architecture.EPISODE LINKSIntroduction to Designing Events and Event StreamsPractical Data Mesh: Building Decentralized Data Architecture with Event StreamsThe Data Dichotomy: Rethinking the Way We Treat Data and ServicesCoding in Motion: Sound & Vision—Build a Data Streaming App with JavaScript and Confluent CloudUsing Event-Driven Design with Apache Kafka Streaming Applications ft. Bobby CalderwoodWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

Is there a better way to manage access to resources without compromising security? New employees need access to a variety of resources within a company's tech stack. But manually granting access can be error-prone. And when employees leave, their access must be revoked, thus potentially introducing security risks if an admin misses one. In this podcast, Kris Jenkins talks to Anuj Sawani (Security Product Manager, Confluent) about the centralized identity management system he helped build to integrate with Apache Kafka® to prevent common identity management headaches and security risks.With 12+ years of experience building cybersecurity products for enterprise companies, Anuj Sawani explains how he helped build out KIP-768 (Secured OAuth support in Kafka) that supports a unified identity mechanism that spans across cloud and on-premises (hybrid scenarios).Confluent Cloud customers wanted a single identity to access all their services. The manual process required managing different sets of identity stores across the ecosystem. Anuj goes on to explain how Identity and Access Management (IAM) using cloud-native authentication protocols, such as OAuth or OpenID Connect, solves this problem by centralizing identity and minimizing security risks.Anuj emphasizes that sticking with industry standards is key because it makes integrating with other systems easy. With OAuth now supported in Kafka, this means performing client upgrades, configuring identity providers, etc. to ensure the applications can leverage new capabilities. Some examples of how to do this are to use centralized identities for client/broker connections.As Anuj continues to build and enhance features, he hopes to recommend this unified solution to other technology vendors because it makes integration much easier. The goal is to create a web of connectors that support the same standards. The future is bright, as other organizations are researching supporting OAuth and similar industry standards. Anuj is looking forward to the evolution and applying it to other use cases and scenarios.EPISODE LINKSIntroduction to Confluent Cloud SecurityKIP-768: Secured OAuth support in Apache KafkaConfluent Cloud Documentation: OAuth 2.0 SupportApache Kafka Security Best PracticesSecurity for Real-Time Data Stream Processing with Confluent CloudWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio PlaylisSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

Can we use machine learning to detect security threats in real-time? As organizations increasingly rely on distributed systems, it is becoming more important to analyze the traffic that passes through those systems quickly. Confluent Hackathon ’22 finalist, Géraud Dugé de Bernonville (Data Consultant, Zenika Bordeaux), shares how his team used TensorFlow (machine learning) and Neo4j (graph database) to analyze and detect network traffic data in real-time. What started as a research and development exercise turned into ZIEM, a full-blown internal project using ksqlDB to manipulate, export, and visualize data from Apache Kafka®.Géraud and his team noticed that large amounts of data passed through their network, and they were curious to see if they could detect threats as they happened. As a hackathon project, they built ZIEM, a network mapping and intrusion detection platform that quickly generates network diagrams. Using Kafka, the system captures network packets, processes the data in ksqlDB, and uses a Neo4j Sink Connector to send it to a Neo4j instance. Using the Neo4j browser, users can see instant network diagrams showing who's on the network, allowing them to detect anomalies quickly in real time.The Ziem project was initially conceived as an experiment to explore the potential of using Kafka for data processing and manipulation. However, it soon became apparent that there was great potential for broader applications (banking, security, etc.). As a result, the focus shifted to developing a tool for exporting data from Kafka, which is helpful in transforming data for deeper analysis, moving it from one database to another, or creating powerful visualizations.Géraud goes on to talk about how the success of this project has helped them better understand the potential of using Kafka for data processing. Zenika plans to continue working to build a pipeline that can handle more robust visualizations, expose more learning opportunities, and detect patterns.EPISODE LINKSZiem Project on GitHub ksqlDB 101 courseksqlDB Fundamentals: How Apache Kafka, SQL, and ksqlDB Work together ft. Simon AuburyReal-Time Stream Processing, Monitoring, and Analytics with Apache KafkaApplication Data Streaming with Apache Kafka and SwimWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join tSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

What happens when you need to store more than a few petabytes of data? Rittika Adhikari (Software Engineer, Confluent) discusses how her team implemented tiered storage, a method for improving the scalability and elasticity of data storage in Apache Kafka®. She also explores the motivating factors for building it in the first place: cost, performance, and manageability. Before Tiered Storage, there was no real way to retain Kafka data indefinitely. Because of the tight coupling between compute and storage, users were forced to use different tools to access cold and hot data. Additionally, the cost of re-replication was prohibitive because Kafka had to process large amounts of data rather than small hot sets.As a member of the Kafka Storage Foundations team, Rittika explains to Kris Jenkins how her team initially considered a Kafka data lake but settled on a more cost-effective method – tiered storage. With tiered storage, one tier handles elasticity and throughput for long-term storage, while the other tier is dedicated to high-cost, low-latency, short-term storage. Before, re-replication impacted all brokers, slowing down performance because it required more replication cycles. By decoupling compute and storage, they now only replicate the hot set rather than weeks of data. Ultimately, this tiered storage method broke down the barrier between compute and storage by separating data into multiple tiers across the cloud. This allowed for better scalability and elasticity that reduced operational toil. In preparation for a broader rollout to customers who heavily rely on compacted topics, Rittika’s team will be implementing tier compaction to support tiering of compacted topics. The goal is to have the partition leader perform compaction. This will substantially reduce compaction costs (CPU/disk) because the number of replicas compacting is significantly smaller. It also protects the broker resource consumption through a new compaction algorithm and throttling. EPISODE LINKSJun Rao explains: What is Tiered Storage?Enabling Tiered StorageInfinite Storage in Confluent PlatformKafka Storage and Processing FundamentalsKIP-405: Kafka Tiered StorageOptimizing Apache Kafka’s Internals with Its Co-Creator Jun RaoWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.